



"""simple docstring"""






import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import ViTMAEForPreTraining, ViTMAEModel
     from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor



class     UpperCAmelCase    :



   def __init__(       self    :	Any							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	Any=1_3							,       __lowerCamelCase    :	Tuple=3_0							,       __lowerCamelCase    :	Optional[Any]=2							,       __lowerCamelCase    :	Optional[int]=3							,       __lowerCamelCase    :	Dict=True							,       __lowerCamelCase    :	Optional[Any]=True							,       __lowerCamelCase    :	Optional[Any]=3_2							,       __lowerCamelCase    :	str=5							,       __lowerCamelCase    :	Tuple=4							,       __lowerCamelCase    :	List[Any]=3_7							,       __lowerCamelCase    :	Dict="gelu"							,       __lowerCamelCase    :	str=0.1							,       __lowerCamelCase    :	Dict=0.1							,       __lowerCamelCase    :	Dict=1_0							,       __lowerCamelCase    :	Any=0.0_2							,       __lowerCamelCase    :	Union[str, Any]=3							,       __lowerCamelCase    :	Any=0.6							,       __lowerCamelCase    :	int=None							,       ):



     """simple docstring"""
     _snake_case   	=      parent
     _snake_case   	=      batch_size
     _snake_case   	=      image_size
     _snake_case   	=      patch_size
     _snake_case   	=      num_channels
     _snake_case   	=      is_training
     _snake_case   	=      use_labels
     _snake_case   	=      hidden_size
     _snake_case   	=      num_hidden_layers
     _snake_case   	=      num_attention_heads
     _snake_case   	=      intermediate_size
     _snake_case   	=      hidden_act
     _snake_case   	=      hidden_dropout_prob
     _snake_case   	=      attention_probs_dropout_prob
     _snake_case   	=      type_sequence_label_size
     _snake_case   	=      initializer_range
     _snake_case   	=      mask_ratio
     _snake_case   	=      scope

     # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
     # (we add 1 for the [CLS] token)
     _snake_case   	=      (image_size // patch_size) ** 2
     _snake_case   	=      int(math.ceil((1 - mask_ratio) * (num_patches + 1)				)				)



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

     _snake_case   	=      None
     if self.use_labels:
       _snake_case   	=      ids_tensor([self.batch_size]							,       self.type_sequence_label_size				)

     _snake_case   	=      self.get_config()

     return config, pixel_values, labels



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     return ViTMAEConfig(
         image_size=self.image_size							,       patch_size=self.patch_size							,       num_channels=self.num_channels							,       hidden_size=self.hidden_size							,       num_hidden_layers=self.num_hidden_layers							,       num_attention_heads=self.num_attention_heads							,       intermediate_size=self.intermediate_size							,       hidden_act=self.hidden_act							,       hidden_dropout_prob=self.hidden_dropout_prob							,       attention_probs_dropout_prob=self.attention_probs_dropout_prob							,       is_decoder=__lowerCamelCase							,       initializer_range=self.initializer_range							,       mask_ratio=self.mask_ratio							,       )



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	Tuple							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      ViTMAEModel(config=__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     _snake_case   	=      model(__lowerCamelCase				)
     self.parent.assertEqual(result.last_hidden_state.shape							,       (self.batch_size, self.seq_length, self.hidden_size)				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	Tuple				):



     """simple docstring"""
     _snake_case   	=      ViTMAEForPreTraining(__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     _snake_case   	=      model(__lowerCamelCase				)
     _snake_case   	=      (self.image_size // self.patch_size) ** 2
     _snake_case   	=      self.patch_size**2 * self.num_channels
     self.parent.assertEqual(result.logits.shape							,       (self.batch_size, num_patches, expected_num_channels)				)

     # test greyscale images
     _snake_case   	=      1
     _snake_case   	=      ViTMAEForPreTraining(__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     _snake_case   	=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
     _snake_case   	=      model(__lowerCamelCase				)
     _snake_case   	=      self.patch_size**2
     self.parent.assertEqual(result.logits.shape							,       (self.batch_size, num_patches, expected_num_channels)				)





   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      self.prepare_config_and_inputs()
     _snake_case	,     _snake_case	,     _snake_case   	=      config_and_inputs
     _snake_case   	=      {'''pixel_values''': pixel_values}
     return config, inputs_dict



@require_torch
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	Any           =  (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
   A__		:	Tuple           =  {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

   A__		:	List[str]           =  False
   A__		:	List[Any]           =  False
   A__		:	Optional[int]           =  False
   A__		:	List[str]           =  False



   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     _snake_case   	=      ViTMAEModelTester(self				)
     _snake_case   	=      ConfigTester(self							,       config_class=__lowerCamelCase							,       has_text_modality=__lowerCamelCase							,       hidden_size=3_7				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     self.config_tester.run_common_tests()



   @unittest.skip(reason='''ViTMAE does not use inputs_embeds'''				)
   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     pass



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       _snake_case   	=      model_class(__lowerCamelCase				)
       self.assertIsInstance(model.get_input_embeddings()							,       (nn.Module)				)
       _snake_case   	=      model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(__lowerCamelCase							,       nn.Linear				)				)



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       _snake_case   	=      model_class(__lowerCamelCase				)
       _snake_case   	=      inspect.signature(model.forward				)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       _snake_case   	=      [*signature.parameters.keys()]

       _snake_case   	=      ['''pixel_values''']
       self.assertListEqual(arg_names[:1]							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_pretraining(*__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	Union[str, Any]				):



     """simple docstring"""
     # make masks reproducible
     np.random.seed(2				)

     _snake_case   	=      int((pt_model.config.image_size // pt_model.config.patch_size) ** 2				)
     _snake_case   	=      np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)
     _snake_case   	=      torch.from_numpy(__lowerCamelCase				)

     # Add `noise` argument.
     # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
     _snake_case   	=      pt_noise

     super().check_pt_tf_models(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       _snake_case   	=      model_class(__lowerCamelCase				)
       model.to(__lowerCamelCase				)
       model.eval()
       # make random mask reproducible
       torch.manual_seed(2				)
       with torch.no_grad():
         _snake_case   	=      model(**self._prepare_for_class(__lowerCamelCase							,       __lowerCamelCase				)				)

       _snake_case   	=      outputs[0].cpu().numpy()
       _snake_case   	=      0

       with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_pretrained(__lowerCamelCase				)
         _snake_case   	=      model_class.from_pretrained(__lowerCamelCase				)
         model.to(__lowerCamelCase				)
         # make random mask reproducible
         torch.manual_seed(2				)
         with torch.no_grad():
           _snake_case   	=      model(**self._prepare_for_class(__lowerCamelCase							,       __lowerCamelCase				)				)

         # Make sure we don't have nans
         _snake_case   	=      after_outputs[0].cpu().numpy()
         _snake_case   	=      0
         _snake_case   	=      np.amax(np.abs(out_a - out_a				)				)
         self.assertLessEqual(__lowerCamelCase							,       1E-5				)



   @unittest.skip(
       reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''				)
   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     pass



   @unittest.skip(
       reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''				)
   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     pass



   @unittest.skip(
       reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''				)
   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     pass



   @unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''				)
   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     pass



   @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     pass





   @slow
   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _snake_case   	=      ViTMAEModel.from_pretrained(__lowerCamelCase				)
       self.assertIsNotNone(__lowerCamelCase				)




def 			snake_case		(		)						->				List[Any]:
  _snake_case   	=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
  return image







@require_torch
@require_vision
class     UpperCAmelCase    (	unittest.TestCase  ):



   @cached_property
   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''				) if is_vision_available() else None





   @slow
   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     # make random mask reproducible across the PT and TF model
     np.random.seed(2				)

     _snake_case   	=      ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''				).to(__lowerCamelCase				)

     _snake_case   	=      self.default_image_processor
     _snake_case   	=      prepare_img()
     _snake_case   	=      image_processor(images=__lowerCamelCase							,       return_tensors='''pt'''				).to(__lowerCamelCase				)

     # prepare a noise vector that will be also used for testing the TF model
     # (this way we can ensure that the PT and TF models operate on the same inputs)
     _snake_case   	=      ViTMAEConfig()
     _snake_case   	=      int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2				)
     _snake_case   	=      np.random.uniform(size=(1, num_patches)				)

     # forward pass
     with torch.no_grad():
       _snake_case   	=      model(**__lowerCamelCase							,       noise=torch.from_numpy(__lowerCamelCase				).to(device=__lowerCamelCase				)				)

     # verify the logits
     _snake_case   	=      torch.Size((1, 1_9_6, 7_6_8)				)
     self.assertEqual(outputs.logits.shape							,       __lowerCamelCase				)

     _snake_case   	=      torch.tensor(
         [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]				)

     self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]							,       expected_slice.to(__lowerCamelCase				)							,       atol=1E-4				)				)











'''simple docstring'''







import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD







def 						_A      ( ):






   '''simple docstring'''







   A__       =     'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
   A__       =     Image.open(requests.get(UpperCAmelCase      ,stream=UpperCAmelCase						).raw						).convert('RGB'						)

   return image







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     []
   # fmt: off

   # vision encoder
   rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding')						)
   rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding')						)
   rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight')						)
   rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias')						)
   rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight')						)
   rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias')						)

   for i in range(config.vision_config.num_hidden_layers						):
      rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.weight""", F"""vision_model.encoder.layers.{i}.layer_norm1.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.bias""", F"""vision_model.encoder.layers.{i}.layer_norm1.bias""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.weight""", F"""vision_model.encoder.layers.{i}.layer_norm2.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.bias""", F"""vision_model.encoder.layers.{i}.layer_norm2.bias""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.attn.qkv.weight""", F"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.weight""", F"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",)						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.bias""", F"""vision_model.encoder.layers.{i}.self_attn.projection.bias""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc1.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc1.bias""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc2.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc2.bias""")						)

   # QFormer
   rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.embeddings.layernorm.weight')						)
   rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias')						)

   # fmt: on
   return rename_keys







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     dct.pop(UpperCAmelCase						)
   A__       =     val







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   for i in range(config.vision_config.num_hidden_layers						):
      # read in original q and v biases
      A__       =     state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias"""						)
      A__       =     state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias"""						)

      # next, set bias in the state dict
      A__       =     torch.cat((q_bias, torch.zeros_like(UpperCAmelCase      ,requires_grad=UpperCAmelCase						), v_bias)						)
      A__       =     qkv_bias







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     364 if 'coco' in model_name else 224
   A__       =     InstructBlipVisionConfig(image_size=UpperCAmelCase						).to_dict()

   # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
   # seems like flan-T5 models don't have bos_token_id properly set?
   if "t5-xl" in model_name:
      A__       =     TaConfig.from_pretrained('google/flan-t5-xl'      ,dense_act_fn='gelu'      ,bos_token_id=1						).to_dict()
   elif "t5-xxl" in model_name:
      A__       =     TaConfig.from_pretrained('google/flan-t5-xxl'      ,dense_act_fn='gelu'      ,bos_token_id=1						).to_dict()
   elif "vicuna-7b" in model_name:
      A__       =     LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'      ,vocab_size=32001						).to_dict()
   elif "vicuna-13b" in model_name:
      A__       =     LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf'      ,vocab_size=32001						).to_dict()
   else:
      raise ValueError('Model name not supported'						)

   # the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
   A__       =     InstructBlipQFormerConfig(vocab_size=30523						).to_dict()
   A__       =     InstructBlipConfig(vision_config=UpperCAmelCase      ,text_config=UpperCAmelCase      ,qformer_config=UpperCAmelCase						)

   return config, image_size







@torch.no_grad()
def 						_A      ( UpperCAmelCase      ,UpperCAmelCase=None      ,UpperCAmelCase=False						):






   '''simple docstring'''







   A__       =     AutoTokenizer.from_pretrained('bert-base-uncased'      ,truncation_side='left'						)
   qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}						)

   if "t5" in model_name:
      A__       =     TaTokenizerFast.from_pretrained('google/flan-t5-xl'      ,truncation_side='left'						)
   elif "vicuna" in model_name:
      # the following was used in the original implementation:
      # tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
      # tokenizer.add_special_tokens({"pad_token": "[PAD]"})
      # tokenizer.add_special_tokens({"bos_token": "</s>"})
      # tokenizer.add_special_tokens({"eos_token": "</s>"})
      # tokenizer.add_special_tokens({"unk_token": "</s>"})
      A__       =     LlamaTokenizerFast.from_pretrained(
          'huggyllama/llama-7b'      ,truncation_side='left'      ,bos_token='</s>'      ,unk_token='</s>'						)
      tokenizer.add_special_tokens({'pad_token': '[PAD]'}						)

   A__  ,			A__       =     get_blipa_config(UpperCAmelCase						)
   A__       =     InstructBlipForConditionalGeneration(UpperCAmelCase						).eval()

   A__       =     {
       'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'),
       'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'),
       'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'),
       'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'),
   }

   A__  ,			A__       =     model_name_to_original[model_name]

   # load original model
   print('Loading original model...'						)
   A__       =     'cuda:1' if torch.cuda.is_available() else 'cpu'
   A__       =     'cuda:2' if torch.cuda.is_available() else 'cpu'
   A__  ,			A__  ,			A__       =     load_model_and_preprocess(
       name=UpperCAmelCase      ,model_type=UpperCAmelCase      ,is_eval=UpperCAmelCase      ,device=UpperCAmelCase						)
   original_model.eval()
   print('Done!'						)

   # update state dict keys
   A__       =     original_model.state_dict()
   A__       =     create_rename_keys(UpperCAmelCase						)
   for src, dest in rename_keys:
      rename_key(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)

   # some keys can be renamed efficiently
   for key, val in state_dict.copy().items():
      A__       =     state_dict.pop(UpperCAmelCase						)
      if key.startswith('Qformer.bert'						):
         A__       =     key.replace('Qformer.bert'      ,'qformer'						)
      if "attention.self" in key:
         A__       =     key.replace('self'      ,'attention'						)
      if "llm_proj" in key:
         A__       =     key.replace('llm_proj'      ,'language_projection'						)
      if "t5_proj" in key:
         A__       =     key.replace('t5_proj'      ,'language_projection'						)
      if key.startswith('llm_model'						):
         A__       =     key.replace('llm_model'      ,'language_model'						)
      if key.startswith('t5'						):
         A__       =     key.replace('t5'      ,'language'						)
      A__       =     val

   # read in qv biases
   read_in_q_v_bias(UpperCAmelCase      ,UpperCAmelCase						)

   # note: weights get loaded in torch.float32 by default
   hf_model.load_state_dict(UpperCAmelCase      ,strict=UpperCAmelCase						)

   A__       =     load_demo_image()
   A__       =     'What is unusual about this image?'

   # create processor
   A__       =     BlipImageProcessor(
       size={'height': image_size, 'width': image_size}      ,image_mean=UpperCAmelCase      ,image_std=UpperCAmelCase						)
   A__       =     InstructBlipProcessor(
       image_processor=UpperCAmelCase      ,tokenizer=UpperCAmelCase      ,qformer_tokenizer=UpperCAmelCase      ,)
   A__       =     processor(images=UpperCAmelCase      ,text=UpperCAmelCase      ,return_tensors='pt'						).to(UpperCAmelCase						)

   # make sure processor creates exact same pixel values
   A__       =     vis_processors['eval'](UpperCAmelCase						).unsqueeze(0						).to(UpperCAmelCase						)
   A__       =     inputs.pixel_values
   assert torch.allclose(original_pixel_values.to(pixel_values.device						)      ,UpperCAmelCase						)

   original_model.to(UpperCAmelCase						)
   hf_model.to(UpperCAmelCase						)
   with torch.no_grad():
      if "vicuna" in model_name:
         A__       =     original_model({'image': original_pixel_values, 'text_input': [prompt]}						).logits
         A__       =     hf_model(**UpperCAmelCase						).logits
      else:
         A__       =     original_model(
             {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}						).logits
         A__       =     tokenizer('\n'      ,return_tensors='pt'						).input_ids.to(UpperCAmelCase						)
         A__       =     label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id      ,-100						)
         A__       =     hf_model(**UpperCAmelCase      ,labels=UpperCAmelCase						).logits

   print('First values of original logits:'      ,original_logits[0, :3, :3]						)
   print('First values of HF logits:'      ,logits[0, :3, :3]						)

   # assert values
   assert original_logits.shape == logits.shape
   A__       =     1e-4 if 'vicuna' in model_name else 1e-5
   assert torch.allclose(original_logits.to(logits.device						)      ,UpperCAmelCase      ,atol=UpperCAmelCase						)
   print('Looks ok!'						)

   print('Generating with original model...'						)
   A__       =     original_model.generate({'image': original_pixel_values, 'prompt': prompt}      ,num_beams=5						)

   # important: we need to cast the weights of the HF model to the appropriate type
   print('Generating with HF model...'						)
   A__       =     hf_model.generate(
       **UpperCAmelCase      ,do_sample=UpperCAmelCase      ,num_beams=5      ,max_length=256      ,min_length=1      ,top_p=0.9      ,repetition_penalty=1.5      ,length_penalty=1.0      ,temperature=1      ,)
   if "vicuna" in model_name:
      # convert output id 0 to 2 (eos_token_id)
      # TODO add this in the generate method?
      A__       =     2
   print('Original generation:'      ,UpperCAmelCase						)
   A__       =     processor.batch_decode(UpperCAmelCase      ,skip_special_tokens=UpperCAmelCase						)
   A__       =     [text.strip() for text in output_text]
   print('HF generation:'      ,UpperCAmelCase						)

   if pytorch_dump_folder_path is not None:
      processor.save_pretrained(UpperCAmelCase						)
      hf_model.save_pretrained(UpperCAmelCase						)

   if push_to_hub:
      processor.push_to_hub(F"""Salesforce/{model_name}"""						)
      hf_model.push_to_hub(F"""Salesforce/{model_name}"""						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    lowerCAmelCase_     						=				[
        '''instructblip-vicuna-7b''',
        '''instructblip-vicuna-13b''',
        '''instructblip-flan-t5-xl''',
        '''instructblip-flan-t5-xxl''',
    ]
    parser.add_argument(
        '''--model_name''',
        default='''instructblip-flan-t5-xl''',
        choices=choices,
        type=str,
        help='''Path to hf config.json of model to convert''',
    )
    parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument(
        '''--push_to_hub''',
        action='''store_true''',
        help='''Whether to push the model and processor to the hub after converting''',
    )

    lowerCAmelCase_     						=				parser.parse_args()

    convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


